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metrics used in text categorization by using local and global policies. For the experiments, we use three datasets which vary in size, complexity and skewness. We use SVM as the classifier and tf-idf weighting for term weighting. We observed that almost in all metrics, local policy outperforms when the number of keywords is
Previous approaches of emotion recognition from text were mostly implemented under keyword-based or learning-based frameworks. However, keyword-based systems are unable to recognize emotion from text with no emotional keywords, and constructing an emotion lexicon is a tough work because of ambiguity in defining all
embedded in different parts of the story. We are proposing a framework for story classification using keyword and Part-of-speech (POS) based features. Keyword based features like Term Frequency (TF) and Term Frequency Inverse Document Frequency (TFIDF) are used. Classification performance is analyzed for different story parts
events. And a huge resource of text-based emotion can be found from the World Wide Web nowadays. This paper reports a study to investigate the effectiveness of using SVM (Support Vector Machine) on linguistic features considering emotion keywords and negative words, and classify a collection of blog posts sentences tagged
Image annotation systems aim at automatically annotating images with some predefined keywords. In this paper, we propose an automatic image annotation approach by incorporating word correlations into multi-class support vector machine (SVM). At first, each image is segmented into five fixed-size blocks or tiles and
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